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Risk in complex genetics: “All models are wrong but some are useful”
Author(s) -
Sawcer Stephen,
Wason James
Publication year - 2012
Publication title -
annals of neurology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.764
H-Index - 296
eISSN - 1531-8249
pISSN - 0364-5134
DOI - 10.1002/ana.23613
Subject(s) - genome wide association study , context (archaeology) , computational biology , genetic association , value (mathematics) , cognitive science , epistemology , computer science , biology , data science , psychology , genetics , philosophy , single nucleotide polymorphism , machine learning , paleontology , gene , genotype
Although genome‐wide association studies (GWAS) have proven remarkably effective at identifying reliably associated genetic variants, the biology underlying these discoveries is rarely immediately apparent and in most cases seems bound to require extensive fine mapping and functional analysis before it is revealed. In this context, it is logical and appropriate to try to interrogate available genetic data for biological insights. However, because such efforts invariably depend upon mathematical modeling, misperceptions can easily arise if the relevant mathematical properties are overlooked or forgotten. In this report, we will examine these mathematical issues, highlight some of the more common misconceptions, and hopefully help to clarify the somewhat blurry distinction between biology and mathematics that can so easily undermine and obscure the value of GWAS discoveries. ANN NEUROL 2012;72:502–509